RISK MANAGEMENT IN IT PROJECTS USING ARTIFICIAL INTELLIGENCE TOOLS

Abstract

The article explores the specifics of risk management in IT projects through the use of artificial intelligence (AI) tools. It analyses recent studies by Ukrainian and international scholars that show a growing interest in applying AI to project management. In particular, combining machine-, deep- and reinforcement-learning techniques within Scrum has been found to improve the accuracy of risk forecasting in agile projects and to highlight AI’s significant role in enterprise digitalization.

 Key risk-management stages where AI is most beneficial—enhancing threat-assessment accuracy, rapidly detecting potential hazards, and selecting effective mitigation strategies—are identified. Special attention is paid to the use of machine-learning, deep-learning and generative-AI algorithms for proactive risk identification, prediction of consequences, and development of response recommendations. The paper presents practical examples of AI-driven risk management in IT projects under both Waterfall and Agile methodologies.

AI adoption is proposed for the stages of risk identification, qualitative and quantitative assessment, response planning, monitoring and control. Illustrative cases show gradient boosting, neural networks and large language models automatically generating risk registers, forecasting the likelihood of risk materialisation, and calculating optimal time and budget reserves.

The prospects of integrating AI tools as an integral part of modern project-management approaches are considered, with emphasis on ensuring algorithmic transparency and developing staff competencies for effective collaboration with these tools. The study concludes that embedding AI into risk-management processes enables a shift from a reactive to a predictive-adaptive project-control model, reducing costs and increasing the probability of achieving objectives in the IT industry.